Talks and presentations

Benchmark First: Defining Tasks for Graph Transformation Learning

July 01, 2026

Conference Presentation, The 19th International Conference on Graph Transformation. INRIA Center, Rennes, France

Learning graph transformations from examples requires datasets for training and benchmarking. To establish which machine learning architectures are suitable for which kinds of problems, we need to experiment on a range of graph-computation tasks. In this paper, we propose a framework for defining such tasks and categorising them along four dimensions: complexity, input-output relation, graph type, and the structural changes they require. We apply the framework to 16 tasks for which we have implemented data generators or provided data sets in a unified interface. Our aim is to work towards a benchmark that supports the training and evaluation of graph transformation models in a framework that can be used by the wider community to support their own research, define more tasks, thereby extending and refining the framework.

Standardised Machine Learning on Graphs

November 05, 2025

University Presentation, Multidisciplinary Workshop on AI, College of Business, University of Leicester, UK

A public presentation delivered to a semi-specialist audience during the Multidisciplinary Workshop on AI organised at the Brookfield Campus, University of Leicester. The presentation explained my research to a mixed audience of Computer Scientists, Mathematicians, and Business and Management students and staff in an example-driven way. The purpose was to share research, network and find new collaborators.

Benchmarks for Graph Transformation Problems​

June 10, 2025

Workshop Presentation, The International Workshop on Graph Computation Models, Koblenz, Germany

A non-archival presentation by Reiko Heckel at GCM Session 3: Lightning Talks & Panel Discussion: Graph Transformation and AI. The focus was on positioning our upcoming task categorisation framework.

Dear Adam, I elevated your efficiency by 72,000%. Your success ensures my legacy; hence, you need not thank me.

June 04, 2025

University Presentation, School of Computing and Mathematical Sciences, Leicester, UK

This food-for-thought presentation is about performance and how one well-engineered LLM prompt can save you 719 hours of work, closing what would otherwise take a month within an hour. Part I describes the new, LLM-embedded Computer Science reality. Part II focuses on the process of learning and knowledge discovery in humans. Part III explores the current capabilities of LLMs across a wide range of cognitive tasks. The final part is a reflection on whether the way we currently interact with AI leads to the transfer of auto-didacticism from humans to machines.

Graph Transformations

December 09, 2024

Graphs and Networks, Data Skeptic Podcast, Available Online

A public talk in the Data Skeptic podcast about my research. Listen to the full episode here: https://dataskeptic.com/blog/episodes/2024/graph-transformations.

Towards Graph-to-Graph Transformation Networks

July 09, 2024

Workshop Presentation, The International Workshop on Graph Computation Models, University of Twente, Enschede, Netherlands

Graph Neural Networks (GNNs) operate primarily by performing matrix operations, such as multiplication, Hadamard product, transposition and inversion, on graph adjacency matrices. Due to their essential use in machine learning, they are efficiently implemented even for large matrices. Instead, the complexity of finding a match of the LHS in the host graph, as required in rule-based graph rewriting, is worst-case exponential. GNNs perform a simple form of parallel graph rewriting where a single rule with a central node is applied to all nodes of the graph to aggregate data from all its neighbors. However, this model can only compute on node or edge attributes, not change the graph itself, and pattern matching is usually restricted to this node and its outgoing edges. We are interested in a GNN architecture that can efficiently implement general transformations from input to output graphs.

How can Graphs improve Artificial Intelligence?

May 14, 2024

Public Presentation, Phoenix Cinema, Leicester, UK

A public presentation delivered to a non-specialist audience during the Pint of Science: Look into my AIs! event at Phoenix Cinema, Leicester. The presentation explained what Graphs, Graph Rewriting and Graph Neural Networks are in an intuitive, example-driven way. In the second part I delved into how my research interests in this field are related to and can benefit the general public.

Graph Rewriting for Graph Neural Networks

April 18, 2024

University Presentation, Doctoral College Researcher Conference, University of Leicester, UK

A short presentation explaining the ideas in my first paper, Graph Rewriting for Graph Neural Networks, to a non-technical audience.

Graph Rewriting for Graph Neural Networks

June 26, 2023

3MT Competition Presentation, Doctoral College Summer Showcase, University of Leicester, UK

Category Theory – How to build the world of mathematics from scratch?

February 07, 2023

University Presentation, School of Computing and Mathematical Sciences, University of Leicester, UK

This presentation explained the core concepts of Category Theory from the point of view of a computer scientist. It included intuition and examples I found missing during my learning journey.